he results may be silently truncated
or otherwise corrupted in the downcast to the lower type. This usage is useful
when one wants to avoid creating large temporary arrays and instead allows one
to reuse the same array memory repeatedly (at the expense of not being able to
use more convenient operator notation in expressions). Note that when the
output argument is used, the ufunc still returns a reference to the result.

 >>> x = np.arange(2)
 >>> np.add(np.arange(2),np.arange(2.),x)
 array([0, 2])
 >>> x
 array([0, 2])

and & or as ufuncs
==================

Invariably people try to use the python 'and' and 'or' as logical operators
(and quite understandably). But these operators do not behave as normal
operators since Python treats these quite differently. They cannot be
overloaded with array equivalents. Thus using 'and' or 'or' with an array
results in an error. There are two alternatives:

 1) use the ufunc functions logical_and() and logical_or().
 2) use the bitwise operators & and \|. The drawback of these is that if
    the arguments to these operators are not boolean arrays, the result is
    likely incorrect. On the other hand, most usages of logical_and and
    logical_or are with boolean arrays. As long as one is careful, this is
    a convenient way to apply these operators.

N)